class_docstr: Early stop single trials when they reached a plateau.
  
  When the standard deviation of the `metric` result of a trial is
  below a threshold `std`, the trial plateaued and will be stopped
  early.
  
  Args:
      metric: Metric to check for convergence.
      std: Maximum metric standard deviation to decide if a
          trial plateaued. Defaults to 0.01.
      num_results: Number of results to consider for stdev
          calculation.
      grace_period: Minimum number of timesteps before a trial
          can be early stopped
      metric_threshold (Optional[float]):
          Minimum or maximum value the result has to exceed before it can
          be stopped early.
      mode: If a `metric_threshold` argument has been
          passed, this must be one of [min, max]. Specifies if we optimize
          for a large metric (max) or a small metric (min). If max, the
          `metric_threshold` has to be exceeded, if min the value has to
          be lower than `metric_threshold` in order to early stop.
code: def stop_all(self):
      return False
  
file_path: ray/python/ray/tune/stopper/trial_plateau.py
imports: from typing import Dict, Optional
  from collections import defaultdict, deque
  import numpy
  from ray.util.annotations import PublicAPI
  from ray.tune.stopper.stopper import Stopper
label_desc: Use image node_id 3 for calling the TrialPlateauStopper obj's underlying member method code with example usage: obj.stop_all() and returns: False